1. Field of the Invention
The present invention relates to a method of phrase verification, and particularly to a method of phrase verification in which a phrase is verified not only according to its own confidence measures as obtained from different methods, but also according to neighboring phrases and their confidence levels.
2. Description of the Related Art
In recent years, the application of speech recognition, as used in voice dialing in cellular phones and speech input in PDAs (personal digital assistants), has been generally used to help users access cellular phones and PDAs in a more convenient way, with operations closer to human nature.
Although substantial progress has been made in speech recognition over the last decade, speech recognition errors are still a major problem in such systems. The spontaneous utterances faced by a spoken dialogue system are frequently disfluent, noisy, or even out-of-domain. These characteristics seriously increase the chance of misrecognition and, consequently, degrade the performance of dialogue system. Therefore, verifying recognized words/phrases is vitally important for spoken dialogue systems.
In past research, two major approaches to word/phrase verification have been used. The first approach uses confidence measures to reject misrecognized words/phrases. The confidence measure of a word/phrase can be assessed by utterance verification or derived from the sentence probabilities of N-best hypotheses. Another approach uses classification models, such as decision tree and neural network, to label words with confidence tags. A confidence tag is either “acceptance” or “rejection”. The features used for classification are usually obtained from the intermediate results in the speech recognition and language understanding phases.
Although various kinds of information have been explored to assess the confidence measure or select the confidence tag for a word/phrase, the contextual confidence information is rarely leveraged. Since correct and incorrect words/phrases tend to appear consecutively, the confidence information of a word/phrase is helpful in assessing the confidence levels of its neighboring words/phrases.
For example, for spoken dialogue providing weather information, the word sequence “weather forecast” occurs frequently in users' queries. If the word “forecast” follows the word “weather” of high-level confidence, it is expected that the confidence level of the word “forecast” is also high. On the other hand, if the confidence level of the word “weather” is low, the confidence level of the word “forecast” is likely to be low.
In order to make use of the contextual confidence information, the invention discloses a novel probabilistic verification model to select confidence tags for concepts (i.e, meaningful phrases).